{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Simple_Linear_Regression.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "dSwpGifpB7W9"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate a random data\n",
        "np.random.seed(0)\n",
        "area = 2.5 * np.random.randn(100) + 25\n",
        "price = 25 * area + 5 + np.random.randint(20,50, size = len(area))\n",
        "data = np.array([area, price])\n",
        "data = pd.DataFrame(data = data.T, columns=['area','price'])\n",
        "plt.scatter(data['area'], data['price'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "t5PZJH8iCFma",
        "outputId": "83ca61ce-230a-4cef-c765-1d5e79dd5c6e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "W = sum(price*(area-np.mean(area))) / sum((area-np.mean(area))**2)\n",
        "b = np.mean(price) - W*np.mean(area)\n",
        "print(\"The regression coefficients are\", W,b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfy1amXuCHBD",
        "outputId": "1cb4a3e8-91c8-48b9-a796-1de63d8e24f0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The regression coefficients are 24.815544052284988 43.4989785533412\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = W * area + b"
      ],
      "metadata": {
        "id": "lPF_CS2qCNjI"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(area, y_pred, color='red',label=\"Predicted Price\")\n",
        "plt.scatter(data['area'], data['price'], label=\"Training Data\")\n",
        "plt.xlabel(\"Area\")\n",
        "plt.ylabel(\"Price\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "LDFVIsvYCRS4",
        "outputId": "0e044f2a-a6cc-4dcb-cfc8-bc45a91a4e6e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f733f40dbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6iqSPcXfW33hqYtk558A996S9VqrxhI83h2+U1REpKIhIXtuZTMZVNXVs+jzxA3v9DSH7lWeYZqUQZhe1RN1HIpK3WjVrKET8QPKwd99ICginnXsTlTPmZVyfQphd1BK1FEQkb+3sCuFod09Y62DAtY9RUlzEjFZ8oLe4F3MByFpQMLNBQPw+kPsDvwBKgR8A0eRE17n7E8E5U4ELgSbgCnd/Olv1E5H8l6oPv65hM1U1dS1+GC+79bv0+uzjhLIB1zwKZpTt4Ad6R59d1JKsdR+5+xp3L3f3cuAQ4HPg4eDlm6OvxQWEwcDZwBDgBOCPZpa/u1uLSNal66tP243kDmYJAWFTt90jrYNdu/CvR+wLwKT7llM5c35eb4/Z3tprTOE44E13fzvNMacB97r7l+6+DlgLHJbmeBEpcGF9+FFJi8+izGCXxI+2yhnzGPXj/6EsSGX90NK6HR6nKHTtFRTOBv4n7vmPzGyFmf3FzKKbmpYBG+KOqQ3KEpjZxWZWbWbVSo8tUtiiK4RTSeheevbZ5DUHixaBO4unjGHdzJNZPGUMC16vb3l1cyeW9aBgZrsC44EHgqLbgAOAcuBd4KbWXM/dZ7t7hbtX9O3bt03rKiL5Z8LIMspSdCPFupfMYOzYxBfdYfTopHMKIZNpNrVHS+FEYJm7vw/g7u+7e5O7bwNuZ3sXUR0Qv4NF/6BMRDq5VFNB/3bd8cmtg23b0q47SDVOUUhrDXZGewSFc4jrOjKzveNeOx14NXj8CHC2mXU1s4HAgcBL7VA/EcmRqpo6KmfOZ+CUx9MO+DZPNNf/K115bfqJWPyH/9e+FhtgTqczrDXYGVldp2BmPYBvApfEFd9oZuWAA+ujr7n7KjO7H1gNbAUuc/fEjj8R6bCar0w+9qC+PLS0LmHryqlzVwKETvmMTQXNMHldKp1hrcHOMG/Ff8x8U1FR4dXV1bmuhoi0oPnexQAGyTmJiKSbXjxlTPILf/kLXHhhYtnLL0NFRZvWtTMws6XuHvofTiuaRSTrwlYmp/o6WtewmcqZ8/lHw2Z6lhRjBjXXj0s+sAN/oc1nCgoiknWtmdljbN/nYPm05GBQtaxWXT1ZpIR4IpJ1qWb2NB8diHYpdWnampSv6MuiLgy49jGtJ8gytRREJKuqaur4fEvybmUlxUV865AyFrxeHxvwrWvYnDJ5XVS0e0mDw9mhoCAiWRM2wAxQWlLMtPFDEj/Uf/1ruOFnCceddu5NvNIveapoSzOVZMcpKIhI1oQNMAP06Nps+8qQaabxrYMwrUmhLZnTmIKIZE2LKSXMkgJC1bJaKmfMw4i0KHp1L2719WXHqaUgIlmTavvKgd2TgwHFxbBlCxNI7hKqnDm/4LfBzBdqKYhI1oSllFh/wynMv/7kxAPdYcuWVl1HqSmyQy0FEcma+JQSV/33dM5YtSDxgJdegkMPbdV1lJoiu5TmQkSybyfzFUnbUpoLEWkTzZPatfhtXcGgw1FQEJFQO5XV9KOPoE+f5IsqIOQ9BQURSdJ80Vldw2buXvJOUhK70LUCKVoHVTV1zAoS3WlMIH8pKIhIktZkNY2tFdhvP3jnnYTXTjvvP/jw4BEcW7WyVXsnSO4oKIhIktYsCutXWpJ+RXJrWhmScwoKIpIk1aKz5hvjhCWvq5wxL+ncFlsZkje0eE1EkqRaLPbdI/alrLSEvT+pDw0IVctqW9/KkLyiloKIJEm7WCxNV1HZ02sybmVoRXJ+UlAQkVATRpbFgkNVTR0TRvVPOubE79/Ca3vuH3te17CZ0pLkBHZheydo9lF+UlAQkbRSBYSw1NYGNGxuTCjr1b2Y608dogDQQSgoiEhqZkxoVpRun4OwAeXuu3ZRQOhAsjbQbGaDzGx53M8nZvYTM+ttZs+a2RvB717B8WZmt5jZWjNbYWajslU3EWnBq6/u0MY3YTTDqGPJWkvB3dcA5QBmVgTUAQ8DU4B57j7TzKYEz68FTgQODH4OB24LfovIDmh1nqKoHQgGJcVFdCvehU2fNya9phlGHUt7TUk9DnjT3d8GTgPmBOVzINY6PQ24yyOWAKVmtnc71U+koETTVNQ1bMbZvoK4qqYu9Ukhu6Dx2mtULatNmp5aXGSUlhRjQFlpCTPOGMb1pw7RngcFoL3GFM4G/id4vJe7vxs8fg/YK3hcBmyIO6c2KHsXEWmVsDQVmxubuOr+V4CQ1BJpsplGv7Vl2urQngcdW9aDgpntCowHpjZ/zd3dzFqVNtHMLgYuBth3333bpI4ihSZVP36Te2LOoQxTW8dPT00n0+Mkf7VH99GJwDJ3fz94/n60Wyj4/UFQXgfsE3de/6AsgbvPdvcKd6/o27dvFqst0rFU1dRROXM+A6c8zi5hH/aBzY1NPDF7rvY6kFDt0X10Dtu7jgAeAc4HZga//xpX/iMzu5fIAPPHcd1MIpJCVU0dv3x0VcIgb1OaD/ew9BQKBhKV1aBgZj2AbwKXxBXPBO43swuBt4FvB+VPACcBa4HPge9ns24ihaD5vgfphAaDDRugf/LCNOm8shoU3P0zoE+zso1EZiM1P9aBy7JZH5FCEzagHCZV8roJ/dX/L4m0olmkA2tpYVhYMIiuOSh+IMVMJOnUlDpbpANLtTDsWyvnpQ0IAI3bnGmPrMpa3aRjUktBJE9lsiJ58rhBSWMKLQWDeM2T14koKIjkUKoP/uYDyKn2NI7f92Dx1KShOg77tzl8sHufpHKRVBQURHIk7IN/0n3LqX77Ixa8Xh+6IjlsT+MJI8tCU1tXzphHfQtjDr26J+99IJ2bxhREciRs5pAD/73kndCdyyBkYDksX5E7uDN53KAWk9Fdf+qQ1lZbCpxaCiI5siMppWMf8rNmwTXXJB/gHrqYrTkDvnvEvpp5JEkUFETaUfwYwi5maVceN2dEBpbD0lNUzpjHPxo20/OXz/DZlq00NqW+bpkS1UkaCgoi7aT5GEJrAgLAuhtOgRsSyx7/31Vc/ew7bA5aHS3NJjJg8ZQxrXpf6Vw0piDSTjJdfRwmVb6i37zwXquuqQ1vpCUZtRTM7OtEdkLby92HmtlwYLy7T89q7UQKSLoxBCN8f+OwYDDw2sci01dr6lo1LqENbyQTmbYUbieyH0IjgLuvILJxjohkKNW39LLSEm4+q5yyuNcvXfJAykVo8Tup9SzJbEppaUkxM84YpnEEaVGmYwrd3f0lSxzg2pqF+ogUrLDVx9Fv79HNaapq6kLXHIStSN7c2ES34l0oKS5qsQtp+fVjd/4GpFPINCh8aGYHELRwzexMtE2mSEapKKLiVx+HHm8W2/oyatBVc/myy64p37/h80ZuPqucWU+vSbm2oUzjCNIKmQaFy4DZwEFmVgesA/41a7US6QAyTUURL+V2lSHTTKuW1bJHmg97iHRJxbcyUrVERDKV0ZiCu7/l7scDfYGD3P0od1+f1ZqJ5Lmw2USbG5u46v5XqKpJ2kk2XMiK5KpltbGd0D77MnUvbfMP/Akjy5hxxjDKSkswIi0EjSNIa2U6++g3wI3u3hA87wVc5e4/y2blRPJZqpk/Te4tthiYMgVuSFx08Px+5Zx79nRK5q7kgep3eOHNj0JnJAEUmYV+4KdsiYhkKNPuoxPd/broE3ffZGYnAQoK0mn1Ky1J2bWTKnkdENpVFD+QvLmxicVvfpT2vbe568NfsiLTKalFZtY1+sTMSoCuaY4XKXiTxw2ipLgo5euZJK87YPJfU+51kI4WoUm2ZBoU7gbmmdmFZnYh8CwwJ3vVEsl/0T78opBv/hD3we0e2jrAna/23q3V76vBY8mmTAeabwB+DRwc/PzK3W/MZsVEOoIJI8u46dsjkloMsQ9uM9il2f9mQWprCG9thIeYiF7dtQhNsivjhHju/iTwZBbrIpK30q1HiF9/UNewmSIz/r3qJiZMfy7xIuedB3MSG9hhaxeOPagvDy2tS5jZFE11PX3CsOzdpAgtBAUz+5u7H2Vmn5KYmsUAd/evZLV2Inkgk/UI0d9T567kteknJl+kWUbUlha9VezXO+NFcSJtybyV6XtbdXGzUuAOYCiRoHIBMA74AVAfHHaduz8RHD8VuBBoAq5w96fTXb+iosKrq6uzVHuRiMqZ80NnGZWVliSmoQ6bVXTNo2CWsIdBqkVm6haS9mJmS929Iuy1FscUzKzIzF7fwff+PfCUux8EjABeC8pvdvfy4CcaEAYTSbI3BDgB+KOZpZ7aIdJOUq1HiJZXVb+TepppUB5tXURbCKn2XxbJtRbHFNy9yczWmNm+7v5Ophc2s57A0cD3gutsAbZYipkawGnAve7+JbDOzNYChwH/l+l7irSl6Ad4qrZ0v9KS0HxFqaaYRj/4WwoyIrmU6ZTUXsAqM5tnZo9Ef1o4ZyCRLqL/Z2Y1ZnaHmfUIXvuRma0ws78Eq6MByoANcefXBmUJzOxiM6s2s+r6+vrmL4u0iaqaOiY/+ErKxWm3PvZbFk89LqHspqO+2+Kag+gYQRitPZB8kOnso5/v4LVHAZe7+4tm9ntgCnAr8CsiYwy/Am4iMtaQEXefTSQ5HxUVFdkbEJFO7ZePrkq5z3GqfQ4yER00VuI6yVctzT7qBvwQ+BqwErjT3TPdR6EWqHX3F4PnDwJT3P39uOvfDkT/b6oD9ok7v39QJtLuNn2evNdxWDConDEvtDXRq3sxXzRuS7l3AqRJoS2SQy21FOYQ2W3teeBEYDDw40wu7O7vmdkGMxvk7muA44DVZra3u0f3YjgdeDV4/Ahwj5n9B9APOBB4qVV3I5IFu25t5O83nZ78gjuTQ2YSGXDy8L3TTitV4jrJVy0FhcHuPgzAzO6k9R/SlwN3m9muwFvA94FbzKycSPfReuASAHdfZWb3A6uJ7Op2mbvv2C7nIjuptKSYhs2NKbuKSoqLmFFTx4SRZVS//RF3L3knNiDtwENL66jYr3filFWRDiDtOgUzW+buo1I9zzWtU5AdkcluaZ/tM4AetW8nlE077mL+s2J87Hl0nULG6xhE8kS6dQottRRGmNkn0esAJcFzrWiWDimj3dLM6NHsvLCB5OgUUk0xlUKSdkqquxe5+1eCn93dvUvcYwUE6XDSLhxLsQvaAVMeD71WdAqppphKIcl0nYJIQQj79t7jy8+T1hxAJCBMnbuSppAu1vgppGGZTjXFVDqqjLOkinQkqcYNmu+Wlmog2QC/b3notZtvhakpplJIspoQL9s00Cxh0iWcg0gm0/+9+Tvs+dmmhPMmn/hjHhj+zRavb8C6mSe3aZ1F2tPODDSLdDjpxg0WTxnDhFH9k85pzZaYGiuQQqagIAUn1ayfxVOPg6mJZZUz5vHZl1thc/IK5jAaK5BCp6AgHVam4walmz9h+S3fSTp/wLWPQcNmiouM4l2Mxm3pu1LLNFYgnYCCgnRI6dYbxCecyyR5XWOT06t7cWi+o6jfnVWuYCCdgoKC5J1MVhynGje46v5X2ObOqzdPpMeWxG6k7595PQsOODT0PRs+b6TILHT66S6GAoJ0GgoKkldStQCq3/6IBa/XxwJFqn0Omtx3KLV1umu20KskUlAUFCSvpGoBxCecq2vYHFlH0OzcHd3nIDp4POvpNSlzGIl0FlrRLHkl1cyh5gHAiawXAOj7z49aHRCKzDAiH/jRhWhamSyiloLkmXTdOM05LbcOSkuK+XJr8mY38SuSo7QyWURBQfJM2FaVYV1Fq/7jTHo0fpFQdtq5N/FKv8Rv9dPGDwEy/6DX5jfS2SkoSF4J+7Z+7EF9eWhpXSxQZNpV1Kt7cVJ+IhFJT0FB8k78t/Xo9NRUaw5wp6qmjpKQXEfXnzqkvaosUjAUFCRvRaen9qmvY/2fL0o+IFhToLEAkbajoCB5a9bTa3ht+olJ5ZUz5iVtc6mxAJG2oaAg+cmMxc2KjrvoNt7ssw+mbS5FskZBQfJPsy0xIXEgWamrRbJHQUHyRwvBALSYTCTbtKJZcm/t2qSAsLZ3/6SA0HwbTBFpe1ltKZhZKXAHMJTI+qMLgDXAfcAAYD3wbXffZGYG/B44Cfgc+J67L8tm/SQPZNA6iNrmroAgkmXZ7j76PfCUu59pZrsC3YHrgHnuPtPMpgBTgGuBE4EDgzKcKSYAABBYSURBVJ/DgduC35LHMklzHWrvveG99xKKxk+9nxXbuqc8pWdJ8c5WV0RakLXuIzPrCRwN3Ang7lvcvQE4DZgTHDYHmBA8Pg24yyOWAKVmtne26ic7L7qOoK5hM872NNdVNXXpTzRLCgi4szJNQAD4bMvWlq8tIjslm2MKA4F64P+ZWY2Z3WFmPYC93P3d4Jj3gL2Cx2XAhrjza4OyBGZ2sZlVm1l1fX19FqsvLUmV5nrW02vCTzBL6i6qWlZL5Yx5DJzyOLuEdCXFa2zy1NcWkTaRzaDQBRgF3ObuI4HPiHQVxbi7k5zrLC13n+3uFe5e0bdv3zarrLROVU1dymymSemvX389KRg8d8ChHHjd40x+4JVYSyNs17MWry0ibSqbYwq1QK27vxg8f5BIUHjfzPZ293eD7qEPgtfrgH3izu8flEmeiXYbpZKwjiDdQHJTeBBItS1m0rVFpM1lraXg7u8BG8wsOqn8OGA18AhwflB2PvDX4PEjwHkWcQTwcVw3k+SRsG6jqOJdjM+3bOWj7j2TAsKwn9yX0U5o29z53Vnl2vBGJAeyPfvocuDuYObRW8D3iQSi+83sQuBt4NvBsU8QmY66lsiU1O9nuW6yg9J24RjUXD8uqbhyxjw+zbDrp19piZLcieRIVoOCuy8HKkJeOi7kWAcuy2Z9pG2k2h0t1T4HZcEHevPNc4qLDBwat23vKopvDSjJnUj704pmabXmexkf8OGGpIBw2+FnxrqKoi2LbsXb/7mVlhQz68wRzJo4grLSkqT9kkUkN5T7SFotvmtn8dSkRl/SuEHPkuKkVsKXW7fFrqUgIJI/FBQ6obBVyNC6/vsJ069gwty5CWWP/N9arn38DWi2A5oZKdczKCCI5BcFhU4mOp00+iFd17CZyQ++ktC3H12ZDCn2Ng5bZObOeGBb125JwWXSfctD66I1ByL5R0GhkwmbTtoYsl4g9Jt8imAQL6w7aNbTa0IHprXmQCT/aKC5k2nNt/PYsa+9lhwQ7r03KSBU1dRROXM+A6c8TuXM+bE8Rc0HpkFrDkTylVoKnUyq6aSpjs2kdQDh3VLNu6C05kAk/5lnkG8mX1VUVHh1dXWuq9GhNP/whvD1Av9z3085cv0riSc3NkKX8O8RlTPnhwabstISFk8Z0zaVF5E2YWZL3T1sDZlaCp1Nqm/t8WXrQhahhbUO4qXqltJgskjHoqBQAFq70U2qtQETRvVPPjjDlmSqbikNJot0LBpo7uB2eKObeKtXJ48dPPpoxgEBNJgsUijUUujg0m10k9FAboYDyS3RYLJIYVBQ6OB2uC//lFPg8ccTy5qaYJcdbzwqZYVIx6eg0MG1ui/fPfmDf9gwWLEiC7UTkY5GYwodXKv68s2SA4K7AoKIxCgodHATRpYx44xh6dNPr1qVPHawdOkOjR2ISGFT91EBSNuX38qB5NZObxWRwqKWQqH63e+SA8K2bS0GhJ2e3ioiHZqCQqFxjwSDSZO2l1199fbyNNJNbxWRzkFBoZCUlCQNJFfOmMfAomMSspamolQVIqKgUAg2bYq0Ar74IlY06/ZnOPhnTyZ0BU26bzk/q1qZ8jKpprEqVYVI56Gg0NEdcAD07h17+mbv/gy49jH+uHZLUleQA3cveSdli0GpKkREs486qr/9DUaPTigacM2jsXGDVMPJDilTYChVhYhkNSiY2XrgU6AJ2OruFWY2DfgBUB8cdp27PxEcPxW4MDj+Cnd/Opv165BCViSfev7vWPnVr2V8iXRjBEpVIdK5tUf30bHuXt5sQ4ebg7LyuIAwGDgbGAKcAPzRzIpCrtd5/fzniQGhogLc+eigYa26jMYIRCSVfBpTOA24192/dPd1wFrgsBzXKT9s3BjpFpo+fXvZJ5/Ayy8DqccCKg/oTfNJqBojEJF0sh0UHHjGzJaa2cVx5T8ysxVm9hcz6xWUlQEb4o6pDcoSmNnFZlZtZtX19fXNXy48ZWWwxx7bn998c6QLaffdY0WpUl3c/YMjufms8vQpMERE4mR1j2YzK3P3OjPbE3gWuBxYA3xIJGD8Ctjb3S8ws1uBJe7+38G5dwJPuvuDqa5f0Hs0L1gAY5rtbaxcRSLSBtLt0ZzVloK71wW/PwAeBg5z9/fdvcndtwG3s72LqA7YJ+70/kFZ5xJdeRwfEJYvV0AQkXaRtaBgZj3MbPfoY2As8KqZ7R132OnAq8HjR4CzzayrmQ0EDgReylb98tI11yQOJFdWRoLBiBG5q5OIdCrZnJK6F/CwRebNdwHucfenzOy/zKycSPfReuASAHdfZWb3A6uBrcBl7t4UeuUC8+gLb3Bq5dcTCz/7DLp3z02FRKTTylpQcPe3gKSvuO5+bppzfg38Olt1ykerrpjKqX+YGXv+s29eykOHj2fGmk1MGKmgICLtSyuac+XNN+FrX2NI8PSBoccz+eSfRJ4EmUk1S0hE2puCQntzh5NOgqeeihUd8qP/ZmOP0oTDlJlURHJBQaE9PflkJCBE3XUXlXVlbAwJAFp1LCK5kE8rmgvXp59C167bA8KwYbBlC5x7rjKTikheUVDItunT4StfiQQBgKVLYcUKKC4GUq9G1niCiOSCuo+y5e9/h0Fx3/avuAJ+//vQQ5WZVETyhYJCW9u2DcaOhXnztpfV1yfmLxIRyVPqPmpLjz0GRUXbA8I990RmGykgiEgHoZZCW/jkEygt3Z6faNQoePFF6KL/vCLSsailsLOmTYOePbcHhOXLI4PJCggi0gF1uk+uqpq6ttmD+PXX4eCDtz+/6ir47W/brqIiIjnQqYJCVU0dU+euZHNjJM9eXcNmps5dCZB5YNi2DY45Bp5/fnvZxo3Qu3cb11ZEpP11qu6jWU+viQWEqM1BnqGMVFVFBpKjAeH++yPdRgoIIlIgOlVLIVU+oRbzDDU0QK9e258ffjgsXhwJECIiBaRTtRRS5RNKm2fopz9NDAgrV8KSJQoIIlKQOlVQaFWeoVWrItti/uY3kedTpkS6ioYObYeaiojkRqfqPooOJqedfdTUBKNHw//93/ayTZsi6xBERApcpwoK0EKeoQcfhIkTtz+fOxdOP719KiYikgc6XVAItWlT4gyi0aNh4ULYpVP1romIdK4xhVCTJycGhNWrYdEiBQQR6ZQ67yffihWRgeToKuSf/zwykBy/SllEpJPpnN1H69fDiBHbnzc0RPIXiYh0clltKZjZejNbaWbLzaw6KOttZs+a2RvB715BuZnZLWa21sxWmNmorFXsK1+BMWPgr3+NtA4UEEREgPbpPjrW3cvdvSJ4PgWY5+4HAvOC5wAnAgcGPxcDt2WtRr17R/Y8GD8+a28hItIR5WJM4TRgTvB4DjAhrvwuj1gClJrZ3jmon4hIp5XtoODAM2a21MwuDsr2cvd3g8fvAXsFj8uADXHn1gZlCczsYjOrNrPq+vr6bNVbRKRTyvZA81HuXmdmewLPmtnr8S+6u5uZt+aC7j4bmA1QUVHRqnNFRCS9rLYU3L0u+P0B8DBwGPB+tFso+P1BcHgdsE/c6f2DMhERaSdZCwpm1sPMdo8+BsYCrwKPAOcHh50P/DV4/AhwXjAL6Qjg47huJhERaQfZ7D7aC3jYzKLvc4+7P2VmLwP3m9mFwNvAt4PjnwBOAtYCnwPfz2LdREQkRNaCgru/BYwIKd8IHBdS7sBl2aqPiIi0rPOmuRARkSQW+YLeMZlZPZEuqI5uD+DDXFeijRTKvRTKfUDh3Euh3Afk/l72c/e+YS906KBQKMysOm7Fd4dWKPdSKPcBhXMvhXIfkN/3ou4jERGJUVAQEZEYBYX8MDvXFWhDhXIvhXIfUDj3Uij3AXl8LxpTEBGRGLUUREQkRkFBRERiFBTamZntY2YLzGy1ma0ysx8H5aE70uWrNPcxy8xeD3bPe9jMSnNd15akupe4168yMzezPXJVx0ykuw8zuzz4u6wysxtzWc9MpPn3VW5mS6K7OZrZYbmuazpm1s3MXjKzV4L7+GVQPtDMXgx2mrzPzHbNdV1j3F0/7fgD7A2MCh7vDvwdGAzcCEwJyqcAN+S6rjt4H2OBLkH5Dfl+H+nuJXi+D/A0kUWSe+S6rjv4NzkWeA7oGry2Z67ruhP38gxwYlB+ErAw13Vt4T4M2C14XAy8CBwB3A+cHZT/Cbg013WN/qil0M7c/V13XxY8/hR4jchmQql2pMtLqe7D3Z9x963BYUuIpEDPa2n+JgA3A9cQ2TAqr6W5j0uBme7+ZfDaB6mvkh/S3IsDXwkO6wn8Izc1zIxH/DN4Whz8ODAGeDAoz6v/3xUUcsjMBgAjiXx7SLUjXd5rdh/xLgCebO/67Iz4ezGz04A6d38lp5XaAc3+Jl8HRgfdFf9rZofmsm6t1exefgLMMrMNwG+BqbmrWWbMrMjMlhPZO+ZZ4E2gIe7LU+guk7mioJAjZrYb8BDwE3f/JP41j7Qp8/6bKaS+DzP7KbAVuDtXdWut+HshUvfrgF/ktFI7IORv0gXoTaTbYjKR1PWWwypmLOReLgUmufs+wCTgzlzWLxPu3uTu5URazYcBB+W4SmkpKOSAmRUT+Yd+t7vPDYpT7UiXt1LcB2b2PeAU4LtBgMt7IfdyADAQeMXM1hP5H3qZmX01d7VsWYq/SS0wN+jKeAnYRiQhW15LcS/nA9HHDxD5kO0Q3L0BWAAcCZSaWXTrgrzaZVJBoZ0F39DuBF5z9/+IeynVjnR5KdV9mNkJRPrgx7v757mqX2uE3Yu7r3T3Pd19gLsPIPLBOsrd38thVdNK82+rishgM2b2dWBX8jzbaJp7+QfwL8HjMcAb7V231jCzvtEZeGZWAnyTyPjIAuDM4LC8+v9dK5rbmZkdBTwPrCTyjQ0i3RQvEpmRsC/BjnTu/lFOKpmBNPdxC9AV2BiULXH3H7Z/DTOX6l7c/Ym4Y9YDFe6etx+maf4mzwF/AcqBLcDV7j4/J5XMUJp7+QT4PZEusS+Af3P3pTmpZAbMbDiRgeQiIl/C73f3fzez/YF7iXTr1QD/Gp0IkGsKCiIiEqPuIxERiVFQEBGRGAUFERGJUVAQEZEYBQUREYlRUBBpJTObEGRNzeuVqSI7QkFBpPXOAf4W/E4Qt0pVpENSUBBphSAXz1HAhcDZQdkxZva8mT0CrA4SoM0ys5eDfSUuiZ5rZvPMbJmZrQwS7onkFX2rEWmd04Cn3P3vZrbRzA4JykcBQ919nZldDHzs7oeaWVdgsZk9A2wATnf3T4INe5aY2SMdJT+UdA5qKYi0zjlE0hMQ/I52Ib3k7uuCx2OB84J0yS8CfYADiWy48hszW0Ek9UQZHShFunQOaimIZMjMehNJwjbMzJxIPhsHHgc+iz8UuNzdn252/veAvsAh7t4Y5FPq1g5VF8mYWgoimTsT+C933y/InroPsA4Y3ey4p4FLg9TPmNnXzawHkZ3CPggCwrHAfu1ZeZFMKCiIZO4c4OFmZQ+RPAvpDmA1kf0XXgX+TKRVfjdQYWYrgfOA17NbXZHWU5ZUERGJUUtBRERiFBRERCRGQUFERGIUFEREJEZBQUREYhQUREQkRkFBRERi/j/fNDsAR9fs6QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bN433O3bCVmH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}